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Adam E Flanders¹, Xindi Wang², Carol C Wu³

¹Department of Radiology, Thomas Jefferson University Hospitals, 132 S Tenth St, Ste 1080 B Main Building, Philadelphia, PA 19107.

Radiology. Artificial intelligence

|April 30, 2025

概括

为医学成像创建有效的人工智能 (AI) 模型是具有挑战性的,因为注释数据有限. 较新的大型语言模型 (LLM) 提供了一个可扩展的解决方案,用于从临床报告中生成准确的标签,从而改善AI模型培训.

科学领域:

医学成像分析医学成像分析
医疗保健中的人工智能
计算机视觉计算机视觉

背景情况:

高质量,多样化的医疗成像数据集与专家注释是训练AI模型的稀缺.
传统的手册注释是耗时的,并且重新使用现有的注释是不切实际的.
以前的自然语言处理 (NLP) 方法需要为每个用例提供定制模型培训.

研究的目的:

审查医学图像注释和标签过程的演变.
突出大型语言模型 (LLM) 在自动化标签生成方面的潜力.
提出一个可扩展的解决方案,用于有效的AI模型培训医疗成像.

主要方法:

审查传统的手动医学图像注释技术.
探索使用自然语言处理 (NLP) 的半自动化方法.
应用大型语言模型 (LLM) 与快速工程来从临床放射学报告中提取标签.

主要成果:

通过LLM,可以直接从临床报告中生成精确的,标准化的标签.
将自动生成的标签与基础图像模型相结合,便于AI模型培训.
与手动注释相比,半自动化方法提供了一种更有效和更可扩展的方法.

结论:

大型语言模型在克服医疗AI数据限制方面取得了重大进展.
从临床报告中自动生成标签简化了强大的AI模型的创建.
这种方法提高了开发AI用于医学成像分析的效率和可扩展性.

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